论文标题
基于在线硬示例采矿的合成语音欺骗检测
Synthetic Voice Spoofing Detection Based On Online Hard Example Mining
论文作者
论文摘要
自动扬声器验证欺骗（ASVSPOOF）挑战系列对于增强欺骗考虑和对策增长至关重要。尽管最近的ASVSPOOF 2019验证结果表明可以识别大多数攻击的重要能力，但对于某些攻击，该模型的识别效果仍然很差。本文介绍了用于检测未知语音欺骗攻击的在线硬采矿（OHEM）算法。 OHEM用于克服数据集中的简单样本和硬样品之间的不平衡。在ASVSPOOF 2019挑战逻辑访问方案的评估集上，呈现的系统提供了相等的错误率（EER）为0.77％。
The automatic speaker verification spoofing (ASVspoof) challenge series is crucial for enhancing the spoofing consideration and the countermeasures growth. Although the recent ASVspoof 2019 validation results indicate the significant capability to identify most attacks, the model's recognition effect is still poor for some attacks. This paper presents the Online Hard Example Mining (OHEM) algorithm for detecting unknown voice spoofing attacks. The OHEM is utilized to overcome the imbalance between simple and hard samples in the dataset. The presented system provides an equal error rate (EER) of 0.77% on the ASVspoof 2019 Challenge logical access scenario's evaluation set.
